Overseas building engineering list pricing method and system and storage medium

Abstract

The invention relates to the technical field of construction engineering cost, in particular to an overseas construction engineering list pricing method and system and a storage medium, and the method comprises the following steps: building list pricing databases according to countries based on collected overseas construction engineering data; establishing a block chain list information base for each construction project by using a block chain technology; obtaining a bidding project list; the list items in the bidding project list are matched with a list pricing database, and the same or similar list items are found out; for the found list item, performing similar traceability in a block chain list information base so as to determine a historical price applicable to the corresponding list item; performing risk level evaluation on the found list item to obtain an evaluation result; and inputting the found list item, the applicable historical price corresponding to the list item and the evaluation result into an artificial intelligence model, and outputting a pricing scheme through the artificial intelligence model. According to the invention, manual operation can be reduced, and the pricing speed and accuracy are improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of construction engineering cost, in particular to an overseas construction engineering list pricing method and system and a storage medium. BACKGROUND

[0002] The overseas construction engineering market is highly competitive and is affected by various factors such as politics, economy, culture and natural environment. The project cycle is long, and it takes a lot of time and effort to price the list during the bidding stage. Engineering bidding pricing is a key link to determine the engineering cost, which directly affects whether the project is awarded, as well as cost control and profit after winning the bid.

[0003] Traditional overseas construction engineering uses the list method for pricing, which is that each participant collects and analyzes market price information manually for pricing. A large amount of work such as engineering quantity calculation, price inquiry and cost allocation needs to be processed, which is large in calculation and complicated in process, and is prone to human errors. Therefore, a new solution is urgently needed. SUMMARY

[0004] The purpose of the application is to overcome the defects of the prior art and provide an overseas construction engineering list pricing method, system and storage medium to solve the problems of large calculation, complicated process and human errors in the existing manual pricing.

[0005] The technical solution to achieve the above purpose is:

[0006] The application provides an overseas construction engineering list pricing method, which comprises the following steps:

[0007] Based on the collected overseas construction engineering data, a list pricing database is established by country;

[0008] Using blockchain technology, a corresponding list information is established for each construction engineering, and a blockchain list information library is formed;

[0009] Obtain the bidding engineering list;

[0010] Match the list items in the bidding engineering list with the established list pricing database to find out the same or similar list items;

[0011] For the found list items, the same source is traced in the blockchain list information library to determine the applicable historical price of the corresponding list items;

[0012] Risk level assessment is performed on the found list items to obtain an assessment result;

[0013] The found list items, the applicable historical price of the corresponding list items and the assessment result are input into an artificial intelligence model, and a pricing scheme is output by the artificial intelligence model.

[0014] The overseas construction engineering bill pricing method is further improved in that the found bill items are subjected to risk level evaluation, including:

[0015] The real-time futures price of the construction materials with futures nature in the found bill items is obtained;

[0016] The obtained real-time futures price is compared with the historical price to obtain a comparison judgment result;

[0017] The time risk coefficient is determined according to the construction period plan of the bidding project;

[0018] The risk level is determined according to the comparison judgment result and the time risk coefficient, and then the evaluation result is obtained.

[0019] The overseas construction engineering bill pricing method is further improved in that, when the found bill items are subjected to risk level evaluation, if the found bill items do not have futures nature, the pricing strategy thereof is defined as referring to the recent historical price.

[0020] The overseas construction engineering bill pricing method is further improved in that the pricing scheme optimization strategy is set for the artificial intelligence model, including:

[0021] If the risk level evaluation result is low risk, the pricing strategy selects the median value of the historical price;

[0022] If the risk level evaluation result is low-medium risk, the pricing strategy selects the median value ± 5% of the historical price;

[0023] If the risk level evaluation result is medium risk, the pricing strategy selects the artificial intelligence model to automatically generate ± 10%;

[0024] If the risk level evaluation result is medium-high risk, the pricing strategy selects the artificial intelligence model to automatically generate ± 15%;

[0025] If the risk level evaluation result is high risk, the pricing strategy selects the artificial intelligence model to automatically generate a risk buffer report.

[0026] The overseas construction engineering bill pricing method is further improved in that the cost of the pricing scheme output by the artificial intelligence model is calculated, and the bidding price list is obtained by summarizing.

[0027] The overseas construction engineering bill pricing method is further improved in that the cost of the pricing scheme output by the artificial intelligence model is calculated, and the bidding price list is obtained by summarizing.

[0028] The overseas construction engineering bill pricing method is further improved in that the cost of the pricing scheme output by the artificial intelligence model is calculated, and the bidding price list is obtained by summarizing.

[0029] a country-specific bill of quantities database established based on the collected overseas construction project data;

[0030] a blockchain bill information library established for construction projects by using blockchain technology, including bill information corresponding to each construction project;

[0031] an acquisition module for acquiring a bid project bill;

[0032] a finding and matching module connected with the acquisition module and the bill of quantities database, for matching bill items in the bid project bill with the established bill of quantities database to find out the same or similar bill items;

[0033] a price determination module connected with the finding and matching module and the blockchain bill information library, for tracing the same kind in the blockchain bill information library for the found bill items to determine the historical price applicable to the corresponding bill item;

[0034] a risk assessment module connected with the price determination module and the finding and matching module, for performing risk level assessment on the found bill items to obtain an assessment result;

[0035] a processing module connected with the risk assessment module, the price determination module and the finding and matching module, for inputting the found bill items, the historical price applicable to the corresponding bill item and the assessment result into an artificial intelligence model to obtain a bill of quantities scheme by the artificial intelligence model.

[0036] The overseas construction project bill of quantities system further comprises a futures price docking module connected with the finding and matching module, a calculation module connected with the futures price docking module and the price determination module, and a time risk determination module connected with the acquisition module, and the calculation module and the time risk determination module are connected with the risk assessment module.

[0037] The futures price docking module is used to acquire real-time futures prices of construction materials with futures nature in the found bill items.

[0038] The calculation module is used to calculate a price deviation degree according to the real-time futures prices and the historical prices.

[0039] The time risk determination module is used to determine a time risk coefficient according to the construction period plan of the bid project.

[0040] The risk assessment module is used to determine a risk level according to the price deviation degree and the time risk coefficient, and further obtain the assessment result.

[0041] Further improvement of the overseas construction engineering bill pricing system of the application is that the artificial intelligence model is provided with a pricing scheme optimization strategy, and the pricing scheme optimization strategy comprises:

[0042] If the risk level evaluation result is low risk, the pricing strategy selects the median value of the historical price;

[0043] If the risk level evaluation result is low-medium risk, the pricing strategy selects the median value ± 5% of the historical price;

[0044] If the risk level evaluation result is medium risk, the pricing strategy selects the artificial intelligence model to automatically generate ± 10%;

[0045] If the risk level evaluation result is medium-high risk, the pricing strategy selects the artificial intelligence model to automatically generate ± 15%;

[0046] If the risk level evaluation result is high risk, the pricing strategy selects the artificial intelligence model to automatically generate a risk buffer report.

[0047] Further improvement of the overseas construction engineering bill pricing system of the application is that the artificial intelligence model is provided with a pricing scheme optimization strategy, and the pricing scheme optimization strategy comprises:

[0048] The overseas construction engineering bill pricing method, system and storage medium of the application have the following beneficial effects:

[0049] The application uses artificial intelligence technology for data processing and pricing optimization, reduces manual operation, improves calculation speed and accuracy, and meets the needs of rapid decision-making of overseas projects.

[0050] The application can effectively cope with price fluctuation risk, real-time interface futures price API, obtain the latest market price information, combine historical price data for risk assessment and pricing optimization, and timely adjust the pricing strategy to reduce the impact of price fluctuations on project cost.

[0051] The information of the application is transparent and cannot be tampered with, and the data is uniformly managed and stored through blockchain technology, ensuring that the data in the pricing process is real and reliable, tamper-proof, traceable, and improving the trust of all parties in the pricing results.

[0052] The application uses the traceability of blockchain technology to record every operation in the pricing process, making it easy to trace and verify in the event of a dispute, providing strong evidence for dispute resolution and improving the efficiency and fairness of dispute resolution.

[0053] The application formulates a reasonable pricing strategy through scientific risk assessment and pricing optimization, ensures project profit, reduces risk, and improves the bid-winning rate of overseas projects. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 It is an architecture diagram of the overseas construction engineering bill of quantities system of the present application.

[0055] Figure 2 It is a data layer sub-process schematic diagram of the overseas construction engineering bill of quantities method and system of the present application.

[0056] Figure 3 It is a processing layer sub-process schematic diagram of the overseas construction engineering bill of quantities method and system of the present application.

[0057] Figure 4 It is an application layer sub-process schematic diagram of the overseas construction engineering bill of quantities method and system of the present application.

[0058] Figure 5 It is a risk calculation flowchart of the overseas construction engineering bill of quantities method and system of the present application.

[0059] Figure 6 It is a strategy generation schematic diagram of the overseas construction engineering bill of quantities method and system of the present application. DETAILED DESCRIPTION

[0060] The present application will be further described below in combination with the drawings and specific embodiments.

[0061] Reference Figure 1 , the present application provides an overseas construction engineering bill of quantities method, system and storage medium, which combines blockchain technology and artificial intelligence technology, interfaces with futures platforms, and is used to solve the problems of large amount of manual calculation, complicated process and easy human error in the traditional overseas construction engineering bidding stage pricing method. The blockchain technology has the characteristics of decentralization, non-tamperability and traceability, which can ensure the authenticity and reliability of the pricing data, realize the unified management and sharing of data; the interface of the futures platform can obtain real-time price information of raw materials and other materials, providing real-time and accurate data support for price fluctuation risk; artificial intelligence can mine and analyze a large amount of historical data through data analysis and machine learning, improve the efficiency and accuracy of pricing, and also can conduct risk assessment and prediction. The overseas construction engineering bill of quantities method and system of the present application will be described below in combination with the drawings.

[0062] The overseas construction engineering bill of quantities system of the application comprises a bill of quantities database, a blockchain bill information library, an acquisition module, a search matching module, a price determination module, a risk assessment module, and a processing module; the search matching module is connected with the acquisition module and the bill of quantities database; the price determination module is connected with the search matching module and the blockchain bill information library; the risk assessment module is connected with the price determination module and the search matching module; the processing module is connected with the risk assessment module, the price determination module, and the search matching module; wherein the bill of quantities database is established based on collected overseas construction engineering data, and the bill of quantities database is established according to countries, and the collected overseas construction engineering data includes bid price lists, engineering quantity data, price data, contract terms, project duration, and local procurement restrictions of historical projects, and corresponding bill of quantities databases are established according to countries to store construction engineering data of corresponding countries, and the bill of quantities database is deployed on a blockchain platform to realize shared and tamper-proof storage of data.

[0063] The blockchain bill database is established by using blockchain technology and for construction engineering, and the blockchain bill database includes bill information corresponding to each construction engineering; each bill information includes detailed information of bill items, corresponding unit price intervals, project formats (such as factory buildings, residences, railways, and stadiums), and local procurement restrictions. Through the traceability of the blockchain, it is convenient to trace the unit prices of the same bill in the same historical project during bidding, and to make suitability judgments, including whether it is the same project format, whether there are local procurement restrictions, etc.

[0064] The acquisition module is used to acquire the bid engineering bill, and the acquired bid engineering bill includes the name, specification, and engineering quantity of each bill item.

[0065] The search matching module is used to match the bill items in the bid engineering bill with the established bill of quantities database to find out the same or similar bill items.

[0066] The price determination module is used to trace the same kind in the blockchain bill information library for the found bill items to determine the applicable historical price of the corresponding bill item, specifically, to trace the same kind in the blockchain bill information library for the bill items to find out the unit price, format information, and local procurement restrictions of the same bill item in the same historical project, and to display the found information for the staff to manually mark the applicable historical price of the bill item, and then to determine the applicable historical price of the bill item.

[0067] The risk assessment module is used to assess the risk level of the found bill items to obtain an assessment result.

[0068] The processing module is used to input the identified list items, their applicable historical prices, and the evaluation results into the AI ​​model, which then generates a pricing solution. The AI ​​model is then used to optimize the pricing of each list item, taking into account factors such as risk, cost targets, and competitive advantage. Preferably, the AI ​​model can generate multiple pricing solutions.

[0069] In a specific embodiment of the present invention, the pricing system of the present invention further includes a futures price docking module connected to the search and matching module, a calculation module connected to the futures price docking module and the price determination module, and a time risk determination module connected to the acquisition module. The calculation module and the time risk determination module are both connected to the risk assessment module.

[0070] The futures price docking module is used to obtain the real-time futures prices of construction materials with futures properties in the found list items; the futures price docking module is connected to the futures price API to obtain the latest reference unit price of the list items when needed, that is, the real-time futures price; the construction materials with futures properties include steel, cement, aluminum, etc.

[0071] The calculation module is used to calculate the price deviation based on the real-time futures price and the historical price; assuming that the real-time futures price of a certain list item is Pt, and the determined historical price is [Pmin, Pmax], the calculation module determines the size of the real-time futures price Pt and the historical prices Pmin and Pmax. When Pt≤Pmax, the price deviation D is calculated as D=(Pt-Pmin) / (Pmax-Pmin); when Pt>Pmax, the price deviation D is calculated as D=1+(Pt-Pmax) / (Pmax-Pmin).

[0072] The time risk determination module is used to determine the time risk coefficient T based on the construction schedule of the bidding project; the time risk determination module determines the time risk coefficient T according to the following formula: T = T0 × (1 = r) d, where r is the baseline risk, ranging from 0.1 to 0.5. The value of r is manually set based on whether the project is a key project and the comprehensive consideration of the project labor cost, and d is the delay month.

[0073] The risk assessment module is used to determine the risk level based on the price deviation and time risk coefficient, and then obtain the assessment results. Specifically, the risk level is divided into five levels, determined by the combination of D and T:

[0074] When D≤0.2 and T≤T0 (T0 is the benchmark time risk coefficient), the risk level is low;

[0075] When 0.2<D≤0.4 or T0<T≤2T0, the risk level is medium-low;

[0076] When 0.4 < D≤0.6 or 2T0 < T≤3T0, the risk level is medium;

[0077] When 0.6 < D≤0.8 or 3T0 < T≤4T0, the risk level is medium-high;

[0078] When D > 0.8 or T > 4T0, the risk level is high.

[0079] Further, the artificial intelligence model is provided with a group price scheme optimization strategy, and the group price scheme optimization strategy comprises:

[0080] If the risk level evaluation result is low risk, the group price strategy selects the median value of the historical price;

[0081] If the risk level evaluation result is medium-low risk, the group price strategy selects the median value ± 5% of the historical price;

[0082] If the risk level evaluation result is medium risk, the group price strategy selects the artificial intelligence model to automatically generate ± 10%;

[0083] If the risk level evaluation result is medium-high risk, the group price strategy selects the artificial intelligence model to automatically generate ± 15%;

[0084] If the risk level evaluation result is high risk, the group price strategy selects the artificial intelligence model to automatically generate a risk buffer report.

[0085] When the risk level evaluation result is high risk, the artificial intelligence model does not give a group price scheme, but triggers a risk buffer mechanism to generate a risk buffer report, displays the high risk source to the staff, and needs the staff to handle the related risk, and then performs risk level evaluation again until the risk level evaluation result is not high risk.

[0086] When the risk level evaluation result is medium-high risk, the artificial intelligence model gives a group price scheme based on the corresponding group price strategy, and also attaches a risk explanation, and the risk explanation comprises a medium-high risk list item and a reason explanation for the existence of the medium-high risk.

[0087] Further, the artificial intelligence model of the present application selects an existing group price model, sets a group price scheme optimization strategy for the group price model, so that the group price scheme optimization strategy can generate an optimized group price scheme. For the artificial intelligence model, several construction engineering data are selected from the list group price database, and the artificial intelligence model learns the selected construction engineering data to realize the rules and patterns in the learning data.

[0088] The artificial intelligence model of the present application optimizes the group price by machine learning method, and the artificial intelligence model can automatically learn the rules and patterns in the historical group price data, combines real-time market price information and risk evaluation results, and generates the optimal group price scheme.

[0089] In an embodiment of the present application, the pricing system further comprises a pricing risk calculation module connected to the futures price docking module and the price determining module, for calculating the pricing risk value of each item in the bill. Specifically, the pricing risk calculation module compares the real-time futures price with the historical price to obtain the pricing risk value. The pricing risk calculation module calculates the pricing risk value R by the following formula, wherein Pt is the real-time futures price corresponding to the item in the bill, Pmin is the minimum value in the historical price corresponding to the item in the bill, Pmax is the maximum value in the historical price corresponding to the item in the bill, and ωt is a time influence factor determined according to the construction period plan, the longer the construction period, the greater the value of ωt, and 0 < ωt≤ 1. The pricing risk value is equal to the relative deviation rate of the real-time futures price and the interval value of the historical price, multiplied by the time influence factor. The pricing risk value can be used to comprehensively evaluate the price fluctuation risk.

[0090] The pricing risk calculation module is connected to the processing unit, and the processing module inputs the pricing risk value of each item in the bill calculated by the pricing risk calculation module to the artificial intelligence model. When the artificial intelligence model generates the pricing scheme, the pricing risk value of each item in the bill is taken as a price attribute and listed in the pricing scheme.

[0091] In an embodiment of the present application, the processing module is further used to define a pricing strategy for the item in the bill without futures, and the pricing strategy is defined as referring to the recent historical price. The processing module inputs the defined pricing strategy to the artificial intelligence model.

[0092] Further, the price determining module can find multiple historical prices with recent dates in the blockchain bill information database according to the item in the bill without futures, and take each historical price with a recent date as the applicable historical price, so as to facilitate the artificial intelligence model to refer to the historical price with a recent date for pricing the item without futures.

[0093] In an embodiment of the present application, the pricing system further comprises a bill item price library. The bill item price library is formed by classifying and standardizing the bid price bill in all overseas construction engineering data collected. Due to the difference in historical prices, the unit price in the bill item price library is represented in the form of an interval, i.e., the price range of each item in the bill is [the minimum unit price, the maximum unit price].

[0094] By establishing the bill item price library, all bill items involved in the building engineering group pricing can be summarized, and convenient query functions are provided for the staff, and the staff can obtain the corresponding bill item and unit price by searching the bill item price library. When a certain bidding engineering is grouped, the staff first establishes the detailed bill of the bidding engineering according to the bill in the bill item price library, then inputs the detailed bill of the bidding engineering into the group pricing system, and lets the group pricing system group the detailed bill of the bidding engineering, and then an optimized group pricing scheme is given.

[0095] In one specific embodiment of the application, the group pricing system of the application further comprises a cost estimation module connected with the processing module, which is used for cost estimation of the group pricing scheme output by the artificial intelligence model, and the bidding price list is obtained by summarizing. Specifically, the cost estimation module calculates the price of each bill item in the group pricing scheme to obtain the highest price, the lowest price and the reasonable price of the bill item, and then associates the bill item with the corresponding highest price, the lowest price, the reasonable price and the group pricing risk value one by one, and finally the bidding price list is obtained by summarizing, which is preferably output in the form of a table.

[0096] As shown in Figure 1 , the group pricing system of the application includes a data layer S1, a processing layer S2 and an application layer S3 in architecture, the data layer S1 is used for realizing data collection and storage, and the corresponding database is established, and the massive overseas building engineering data collected or collected is stored into the corresponding bill component database according to the country division, and Figure 2 , the bill component database of the country is constructed; a large number of historical engineering bidding price lists are sorted to obtain a bill item price library; and the corresponding bill information is constructed for each building engineering by using the block chain technology to obtain a block chain bill information library. The established bill component database of each country, the bill item price library and the block chain bill information library are stored into the block chain platform, and the data sharing is realized to the corresponding client through the interface provided by the block chain platform.

[0097] The database of the application is stored by means of the block chain platform, and the decentralization, non-tamperability and traceability of the block chain can ensure the authenticity and reliability of the data in the sharing process, which is not affected by any factor. This not only lays a solid data foundation for subsequent group pricing work, but also provides a fair and transparent data environment for all parties, effectively improving the credibility and utilization value of the data.

[0098] The present application forms a unique and characteristic bill item price library when constructing the bill item price library, wherein the unit price of each bill item is presented in the form of an interval, i.e., [the lowest unit price, the highest unit price]. This presentation fully considers the fluctuation of historical prices and provides more valuable data for subsequent pricing. At the same time, the blockchain bill library module takes advantage of the powerful blockchain technology to construct a past historical engineering bill library. In this bill library, each bill records the key information of the bill item in detail, including not only the unit price interval but also important contents such as project industry (e.g., factory building, residence, railway, and stadium) and territorial procurement restrictions. In the actual bidding scene, with the traceability of the blockchain, the staff can conveniently trace the unit price of similar historical engineering bills and make suitability judgments from multiple dimensions such as project industry and territorial procurement restrictions, thereby screening the most applicable historical price data and providing accurate references for bidding pricing.

[0099] The present application conducts corresponding risk analysis on the historical price of the bill item, real-time futures price, and engineering duration, which can help the bidding team accurately grasp the price fluctuation risk and provide strong support for subsequent development of reasonable pricing strategies. Figure 3 , Figure 5 and Figure 6 As shown, the artificial intelligence model is built-in with advanced machine learning algorithms and has strong data learning and analysis capabilities. It can automatically mine potential laws and patterns in historical pricing data and combine current real-time market price information and risk assessment results to generate targeted pricing schemes for different risk levels. For bill items with low risk levels, the historical median is directly used as the basis for pricing, which simplifies the pricing process to the greatest extent while ensuring reasonable prices. For bill items with low-to-medium risk levels, ±5% floating adjustment is made based on the historical median, which takes into account risk factors and retains a certain price elasticity. For bill items with medium risk levels, ±10% fluctuation schemes are generated with the help of AI technology, which realizes precise regulation of prices through intelligent algorithms. For bill items with high-to-medium risk levels, in addition to the ±15% fluctuation scheme generated by AI, detailed risk explanations are also attached to help the bidding team fully understand the risk situation. For bill items with high risk levels, ±20% fluctuation schemes are used with enhanced risk buffering to effectively cope with possible severe price fluctuations and ensure project cost controllability. These different risk level pricing schemes fully reflect the precision and flexibility of artificial intelligence in the pricing optimization process and provide a strong guarantee for developing scientific and reasonable bidding prices.

[0100] As shown, the artificial intelligence model is built-in with advanced machine learning algorithms and has strong data learning and analysis capabilities. It can automatically mine potential laws and patterns in historical pricing data and combine current real-time market price information and risk assessment results to generate targeted pricing schemes for different risk levels. For bill items with low risk levels, the historical median is directly used as the basis for pricing, which simplifies the pricing process to the greatest extent while ensuring reasonable prices. For bill items with low-to-medium risk levels, ±5% floating adjustment is made based on the historical median, which takes into account risk factors and retains a certain price elasticity. For bill items with medium risk levels, ±10% fluctuation schemes are generated with the help of AI technology, which realizes precise regulation of prices through intelligent algorithms. For bill items with high-to-medium risk levels, in addition to the ±15% fluctuation scheme generated by AI, detailed risk explanations are also attached to help the bidding team fully understand the risk situation. For bill items with high risk levels, ±20% fluctuation schemes are used with enhanced risk buffering to effectively cope with possible severe price fluctuations and ensure project cost controllability. These different risk level pricing schemes fully reflect the precision and flexibility of artificial intelligence in the pricing optimization process and provide a strong guarantee for developing scientific and reasonable bidding prices. Figure 4As shown, the cost estimation module plays a key role in the entire bid process. It receives the bid scheme generated by the AI bid optimization module and comprehensively summarizes the prices of each list item. During the summary process, not only the unit price of the list item is considered, but also the corresponding engineering quantity and other factors. After completing the summary, the cost estimation module uses professional calculation methods to accurately calculate the highest value, lowest value, and reasonable value of the price. Through comprehensive analysis and evaluation of these data, the final tender offer list containing detailed price information is output. During the calculation and output process, the module fully considers risk factors, cost targets, market competition, and other factors to ensure that the output tender offer list not only meets the cost requirements of the project but also has certain market competitiveness.

[0101] The present application also provides an overseas construction engineering list bid method, which will be described below.

[0102] The bid method of the present application comprises the following steps:

[0103] Based on the collected overseas construction engineering data, a list bid database is established by country;

[0104] Using blockchain technology, a corresponding list information is established for each construction project, and a blockchain list information library is formed;

[0105] Obtain the tender project list;

[0106] Match the list items in the tender project list with the established list bid database to find out the same or similar list items;

[0107] For the found list items, trace the same type in the blockchain list information library to determine the applicable historical price of the corresponding list item;

[0108] Risk level assessment is performed on the found list items to obtain the assessment result;

[0109] The found list items, the applicable historical price of the corresponding list item, and the assessment result are input into an artificial intelligence model, and a bid scheme is output by the artificial intelligence model.

[0110] In one specific embodiment of the present application, the risk level assessment of the found list items comprises:

[0111] Obtain the real-time futures price of the construction materials with futures nature in the found list items;

[0112] Compare the obtained real-time futures price with the historical price to obtain a comparison judgment result;

[0113] Determine the time risk coefficient according to the construction period plan of the tender project;

[0114] According to the comparison judgment result and the time risk coefficient, the risk level is determined, and then the evaluation result is obtained.

[0115] In one embodiment of the application, when the found list items are evaluated for risk level, if the found list items do not have futures nature, the pricing strategy is defined as referring to the recent historical price.

[0116] In one embodiment of the application, the artificial intelligence model is also set to have a pricing scheme optimization strategy:

[0117] If the risk level evaluation result is low risk, the pricing strategy selects the median of the historical price;

[0118] If the risk level evaluation result is low-medium risk, the pricing strategy selects the median of the historical price ± 5%;

[0119] If the risk level evaluation result is medium risk, the pricing strategy selects the artificial intelligence model to automatically generate ± 10%;

[0120] If the risk level evaluation result is medium-high risk, the pricing strategy selects the artificial intelligence model to automatically generate ± 15%;

[0121] If the risk level evaluation result is high risk, the pricing strategy selects the artificial intelligence model to automatically generate a risk buffer report.

[0122] In one embodiment of the application, the cost of the pricing scheme output by the artificial intelligence model is also calculated, and the bid price list is obtained.

[0123] The pricing process of the pricing method of the application is described below.

[0124] First, obtain the bid project list, including the name, specification, engineering quantity and other information of each list item.

[0125] Second, match the list with the historical database: match the list items in the bid list with the overseas construction engineering list pricing database by country, and check if there are the same or similar list items.

[0126] Third, block chain list library matching and same kind tracing: for the matched list items, same kind tracing is performed in the block chain list library, the unit price, industry information, local procurement restrictions, etc. of the same list in the same kind of historical engineering are viewed, and manual marking is performed to determine whether the historical price is applicable.

[0127] The fourth step is to identify the construction period and the group price: identify the construction period plan of the project, import the real-time futures price information for the list items with futures attributes, combine the historical prices in the blockchain library for group pricing, and perform risk labeling and evaluation according to the above group pricing risk calculation formula and risk dimension evaluation algorithm.

[0128] The fifth step is artificial intelligence group pricing optimization: using artificial intelligence driven group pricing optimization algorithm to optimize the preliminary group pricing results, considering risk factors, cost targets, market competition and other factors, to generate multiple group pricing schemes.

[0129] The sixth step is cost estimation and formation of final data: cost estimation of the optimized group pricing scheme to determine the highest value, lowest value and reasonable value of each list item price, and form the final group pricing data.

[0130] The application also provides a storage medium, which stores an overseas construction engineering list group pricing method program, and the steps of the overseas construction engineering list group pricing method are realized when the overseas construction engineering list group pricing method program is executed by a processor.

[0131] The characteristics of the group pricing method and system of the application include:

[0132] Technical integration innovation: integrating blockchain, futures platform interface and artificial intelligence technology in the field of overseas construction engineering group pricing, breaking through the traditional single technology application mode, and building a new group pricing technology system.

[0133] Data management upgrade: using blockchain to store construction engineering data by country, realizing data decentralization, non-tamperability and traceability; the list item price library presents unit price in interval form, which is more consistent with the actual situation of historical price fluctuations.

[0134] Intelligent risk control: a group pricing risk calculation model based on futures prices, historical prices and construction period is designed, combined with five-level risk dimension identification, and the risk level is accurately quantified; different group pricing strategies are developed for different risk levels to realize intelligent risk response.

[0135] The advantages of the group pricing method and system of the application compared with the prior art include:

[0136] Information management advantage: traditional technology data is scattered and easy to tamper, the application uses blockchain technology to uniformly manage data, ensures data authenticity and consistency, solves the problem of information opacity, and improves the trust of all parties in the group pricing results.

[0137] Price risk response advantage: traditional methods are difficult to obtain market price dynamics in time, the application can adjust the group pricing strategy in time by real-time docking of futures price API and combining with the risk evaluation model, effectively reducing the cost risk caused by the price fluctuation of raw materials.

[0138] Efficiency and accuracy advantages: traditional manual pricing calculation is large in amount and prone to errors, the present application learns the law of historical data automatically with the help of artificial intelligence algorithm, quickly generates the optimal pricing scheme, greatly improves the pricing efficiency and accuracy, and meets the rapid decision-making needs of overseas projects.

[0139] Dispute resolution advantages: traditional technology lacks effective dispute resolution mechanism, the present application relies on the traceability of blockchain to leave traces throughout the pricing process, which facilitates quick traceability and verification in disputes and improves the efficiency of dispute resolution.

[0140] The beneficial effects of the pricing method and system of the present application include:

[0141] Economic benefits: through accurate risk assessment and reasonable pricing strategy, cost overruns caused by price fluctuations are avoided, project profit space is guaranteed; at the same time, the competitiveness of the bid price is improved, the winning rate of overseas projects is increased, and direct economic benefits are brought.

[0142] Management benefits: realize efficient sharing and unified management of data, reduce information transmission cost and error; intelligent pricing process reduces dependence on manpower, optimizes enterprise internal management process, and improves management efficiency.

[0143] Social benefits: promote the technological upgrading of overseas construction engineering industry, and promote the application and development of new technologies such as blockchain and artificial intelligence in the field of engineering.

[0144] The above embodiments of the present application are described in detail in combination with the drawings, and those of ordinary skill in the art can make various changes to the present application according to the above description. Therefore, some details in the embodiments should not constitute a limitation on the present application, and the scope of protection of the present application will be defined by the appended claims.

Claims

1. A method for pricing overseas construction project bills, characterized by: The steps include: Based on the collected overseas construction project data, a list price database is established by country; Use blockchain technology to establish corresponding inventory information for each construction project, thereby forming a blockchain inventory information database; Obtain a list of bidding projects; Match the list items in the bidding project list with the established list price database to find the same or similar list items; For the found list items, similar tracing is performed in the blockchain list information database to determine the historical price applicable to the corresponding list items; Conduct risk level assessment on the identified checklist items and obtain assessment results; The found list items, the applicable historical prices of the corresponding list items and the evaluation results are input into the artificial intelligence model, and the pricing plan is output through the artificial intelligence model.

2. The overseas construction project bill of materials pricing method according to claim 1, characterized in that: Conduct risk level assessment on the identified checklist items, including: Obtaining real-time futures prices of construction materials with futures characteristics in the found list items; Compare the obtained real-time futures prices with historical prices to obtain comparative judgment results; Determine the time risk factor based on the construction schedule of the bidding project; The risk level is determined based on the comparative judgment results and the time risk coefficient, and then the assessment result is obtained.

3. The overseas construction project bill of materials pricing method according to claim 1, characterized in that: When evaluating the risk level of the identified list items, if the identified list items do not have the nature of futures, their pricing strategy is defined as referring to recent historical prices.

4. The overseas construction project bill of materials pricing method according to claim 1, characterized in that: It also includes setting pricing scheme optimization strategies for artificial intelligence models: If the risk level assessment result is low risk, the price setting strategy uses the median of historical prices; If the risk level assessment result is medium-low risk, the price setting strategy will use the median of the historical price ±5%; If the risk level assessment result is medium risk, the pricing strategy will be automatically generated by the artificial intelligence model with ±10%; If the risk level assessment result is medium-high risk, the pricing strategy will be automatically generated by the artificial intelligence model with ±15%; If the risk level assessment result is high risk, the pricing strategy will use the artificial intelligence model to automatically generate a risk buffer report.

5. The overseas construction project bill of materials pricing method according to claim 1, characterized in that: It also includes cost estimation of the pricing plan output by the artificial intelligence model and summarizing it to obtain a bid quotation list.

6. A storage medium, characterized in that The storage medium stores a program of the overseas construction project bill of materials pricing method. When the program of the overseas construction project bill of materials pricing method is executed by the processor, the steps of the overseas construction project bill of materials pricing method as described in any one of claims 1 to 5 are implemented.

7. An overseas construction project list price system, characterized by: include: A country-specific list price database based on collected overseas construction project data; A blockchain inventory information database for construction projects established using blockchain technology, including inventory information corresponding to each construction project; Acquisition module, used to obtain the bidding project list; A search and matching module connected to the acquisition module and the list price combination database is used to match the list items in the bidding project list with the established list price combination database to find the same or similar list items; a price determination module connected to the search and matching module and the blockchain inventory information repository, configured to perform similar tracing in the blockchain inventory information repository for the found inventory item to determine a historical price applicable to the corresponding inventory item; A risk assessment module connected to the price determination module and the search and matching module, configured to perform risk level assessment on the found list items and obtain an assessment result; The processing module connected to the risk assessment module, the price determination module and the search and matching module is used to input the found list items, the historical prices applicable to the corresponding list items and the evaluation results into the artificial intelligence model, and obtain the pricing plan through the artificial intelligence model.

8. The overseas construction project bill of materials pricing system according to claim 7, characterized in that: It also includes a futures price docking module connected to the search and matching module, a calculation module connected to the futures price docking module and the price determination module, and a time risk determination module connected to the acquisition module, wherein the calculation module and the time risk determination module are both connected to the risk assessment module; The futures price docking module is used to obtain the real-time futures prices of the building materials with futures properties in the found list items; The calculation module is used to calculate the price deviation based on the real-time futures price and the historical price; The time risk determination module is used to determine the time risk coefficient based on the construction period plan of the bidding project; The risk assessment module is used to determine the risk level according to the price deviation and the time risk coefficient, and then obtain the assessment result.

9. The overseas construction project bill of materials pricing system according to claim 7, characterized in that: The artificial intelligence model is set with a pricing scheme optimization strategy, which includes: If the risk level assessment result is low risk, the price setting strategy uses the median of historical prices; If the risk level assessment result is medium-low risk, the price setting strategy will use the median of the historical price ±5%; If the risk level assessment result is medium risk, the pricing strategy will be automatically generated by the artificial intelligence model with ±10%; If the risk level assessment result is medium-high risk, the pricing strategy will be automatically generated by the artificial intelligence model with ±15%; If the risk level assessment result is high risk, the pricing strategy will use the artificial intelligence model to automatically generate a risk buffer report.

10. The overseas construction project bill of materials pricing system according to claim 7, characterized in that: It also includes a cost calculation module connected to the processing module, which is used to perform cost calculation on the pricing plan output by the artificial intelligence model and summarize it to obtain a bid quotation list.

Images